Tell the AI study planner what to test and who to test with. Create a study from a Website URL, Figma URL, PostHog context, or screenshots; InsightLab generates archetyped synthetic users, runs the modeled simulation, and returns replay-backed insights with a Trust Report.
Set up the product target, audience, tasks, and feedback metrics. Demo mode simulates from source context; it does not render or record the submitted target.
The report keeps the run context visible: study source, synthetic users generated, simulation status, replay status, selected behavioral metrics, and evidence-backed recommendations.
The demo follows the same product path from source selection to Trust Report.
Start from a Website URL, Figma URL, PostHog context, or screenshots and describe the target audience, task, and study goal.
InsightLab detects the vertical and recommends universal plus vertical-specific behavioral metrics from the controlled catalog.
Run the simulation, then inspect the replay of task progression, hesitation, drop-off, and interaction evidence.
See evidence-backed recommendations with confidence, assumptions, provenance, and business impact readiness.
InsightLab gives teams a simulation-backed read on a flow before the next round of production testing or research.
Watch where each synthetic user continues, hesitates, or drops at each task step. The replay shows the simulated path behind every summary.
InsightLab detects the study vertical and pulls from a controlled metric catalog: universal metrics plus vertical-specific ones for your context.
After the simulation, inspect the step-by-step replay for each archetype. Each recommendation links to the specific evidence that generated it.
Every recommendation carries a confidence score, explicit assumptions, and a provenance trail, including a flag for whether it's ready to support a business impact claim.
No recruiting. No scheduling. No waiting on design partners. Submit a URL or prototype, define your audience, and get behavioral signal before your next sprint.
Submit a Website URL, Figma link, PostHog context, or screenshots and get a synthetic-user run on demand. No participant calendars to coordinate.
Scrappy Hackers, Risk-Averse Scalers, and ROI Maximizers walk the same flow simultaneously, so you see where each type hesitates before anyone ships.
Universal and vertical-specific metrics are selected from a known catalog, then reviewed through the synthetic replay before insights are summarized.
Evidence, confidence, assumptions, provenance, and business impact readiness are part of the recommendation surface.
Directional product markers, not measured customer outcomes.
InsightLab can work with product URLs, Figma prototypes, optional PostHog context, and uploaded screenshots. Access controls, PII handling, and deletion on request are built in from the start.
Read about our security postureInsightLab runs directional synthetic-user simulations, not direct measurements from live users. Every recommendation is paired with evidence, confidence, assumptions, and provenance so your team can see how much weight to give the finding.
Create a study with a Website URL, Figma URL, PostHog context, or screenshots, then add the audience, task, and study goal. InsightLab uses that study setup to detect the vertical, select metrics, and generate the synthetic-user run. Demo replay is modeled from context, not captured from a live target.
No. InsightLab helps narrow questions and surface likely friction before a live validation round. Live user research and post-launch measurement remain important for final confirmation.
Most studies complete in under 6 minutes. The conversational study planner collects your brief in about 2 minutes; simulation and report generation run in the background. You can close the tab and come back for results.
Study context is scoped to your workspace, encrypted in transit and at rest, and deletable on request. PII in optional source context can be excluded or anonymized before a run. See the Security page for a full breakdown.
InsightLab selects metrics from a controlled catalog rather than generating them arbitrarily. Universal metrics apply across studies, while vertical-specific metrics are added after automatic vertical detection. The demo shows why each metric was selected and which source signals informed that choice.
Each Trust Report surfaces the relevant behavioral metrics, replay-backed findings, evidence for each recommendation, confidence, assumptions, provenance, and whether the finding is ready to support a business impact claim.
Synthetic users are generated from the study brief and selected source context across Scrappy Hackers, Risk-Averse Scalers, and ROI Maximizers. A single run can surface how different user archetypes respond to the same flow, and the replay lets you inspect the simulated path behind the summary.
Surveys and NPS capture stated preferences. InsightLab simulates task behavior against a concrete flow, then shows replay evidence, metric movement, and trust context for each recommendation.
Yes. Choose Figma URL as the study source, add the task and audience, review the automatically selected metrics, then generate synthetic users from that prototype context. In demo mode, the replay does not render interactive Figma frames.
A recommendation points back to the replay, selected behavioral metrics, and the assumptions used to interpret the simulated run. Provenance is included so the report explains why it recommends something.
InsightLab combines source selection, automatic vertical detection, controlled behavioral metrics, synthetic-user generation, simulation replay, insights, and a Trust Report in one flow. The recommendation is not just a summary; it explains its evidence and limits.
We can walk through a study setup, simulation replay, insights, and Trust Report in the live demo.